Continual Learning: Less Forgetting, More OOD Generalization via Adaptive Contrastive Replay

Split CIFAR-100 Split Mini-ImageNet Split Tiny-ImageNet 20 15 10 5 0 GEM A-GEM ER GSS GDUMB HAL MetaSP SOIF Ours Figure 1: Evaluating Out-of-Distribution (OOD) Generalization Capability: The performance of state-of-the-art rehearsal-based methods on the Split CIFAR-100, Split Mini-ImageNet, and Split Tiny-ImageNet datasets significantly drops on OOD samples, highlighting their lack of generalization. In this paper, we address this challenge by proposing a method that consistently outperforms existing approaches across all datasets. Machine learning models often suffer from catastrophic forgetting of previously learned knowledge when learning new classes. Various methods have been proposed to mitigate this issue. However, rehearsal-based learning, which retains samples from previous classes, typically achieves good performance but tends to memorize specific instances, struggling with Out-of-Distribution (OOD) generalization. This often leads to high forgetting rates and poor generalization. Surprisingly, the OOD generalization capabilities of these methods have been largely unexplored. In this paper, we highlight this issue and propose a simple yet effective strategy inspired by contrastive learning and data-centric principles to address it. We introduce Adaptive Contrastive Replay (ACR), a method that employs dual optimization to simultaneously train both the encoder and the classifier. ACR adaptively populates the replay buffer with misclassified samples while ensuring a balanced representation of classes and tasks. By refining the decision boundary in this way, ACR achieves a balance between stability and plasticity.